The development of four-dimensional digital subtraction angiography was a major step towards reliable and readily interpretable images of a patient's vascular system in capture regions of interest. Using an X-ray device, (e.g., an X-ray device with a C-arm), with one or more rotations at different projection angles, two-dimensional projection images of the capture region of interest of the patient's vascular system are here captured while a contrast agent in the form of a contrast agent bolus moves through the vascular system in the capture region. Digital subtraction angiography projection images are obtained by subtraction of a mask image captured without contrast agent, it also being possible to perform subtraction for respective reconstructed three-dimensional image data sets. While it was known at the outset of digital subtraction angiography to produce a plurality of temporally successive three-dimensional image data sets by using digital subtraction angiography projection images captured in a specific time interval to reconstruct therefrom a three-dimensional image data subset, more recent approaches capable of yielding better image quality as well as better temporal resolution have since come into being.
One of these approaches has been described in an article by B. Davis et al., “4D Digital Subtraction Angiography: Implementation and Demonstration of Feasibility,” DOI:10.3174/ajnr.A3529. This approach proposes, initially using a large proportion of the digital subtraction angiography projection images which show the at least largely filled vessels, to reconstruct a non-time-resolved three-dimensional vessel data set which shows the entire vascular system in the capture region. The vessel data set forms the basis for continuously updating the voxel values by multiplicative embedding of the time information from the in particular normalized digital subtraction angiography projection images, such that a series of time-resolved 3D images, (e.g., of image data subsets of the four-dimensional angiography data set), is obtained. In other words, the vessel data set is ultimately used to restrict the reconstruction of the individual three-dimensional image data subsets which incorporate the temporal information from the digital subtraction angiography projection images. Multiplicative back-projection is therefore carried out. This may be understood to mean that voxels which show vessels and are located on the beam of a pixel of a projection image which shows filling with contrast agent are highlighted as filled with contrast agent at the instant of capture of the projection image.
The four-dimensional reconstruction algorithm, described in overall terms in this manner, includes a three-dimensional image reconstruction act known from digital subtraction angiography for establishing the vessel data set. Producing a high-quality vessel data set entails, as has already been described, a sufficient number of projection images which are consistently filled with contrast agent. In order to provide this, it is known, on the one hand, to use an image capture protocol which captures an expanded angular range, (e.g., conventionally >200°), and, on the other hand, a relatively long injection of contrast agent, (e.g., over about 7 seconds), the requirements regarding duration of contrast agent administration (and thus also the size of the bolus) being determined by blood circulation times in the capture zone of interest, (e.g., in the brain).
The described prerequisites for obtaining a high quality three-dimensional vessel data set as the basis for the four-dimensional angiography data set may, however, lead to overlapping flow phases. More precisely, there may be a temporal overlap between the arterial and the venous phase in four-dimensional digital subtraction angiography, which results in the limitation that the arterial and venous structures cannot be separately visualized and evaluated. In addition, film-like viewing of the time series of image data subsets creates impressions which are difficult to evaluate.
Remedying this problem by shortening the contrast agent injection time, therefore using a shorter bolus, is only possible with a severe reduction in quality. If, for example, injection times in the range of 0.5 to 2 seconds as are conventional in two-dimensional digital subtraction angiography are used, arterial inflow and venous outflow may indeed be clearly differentiated, but the image quality of the three-dimensional vessel data set, which is after all used as the restriction data set for the four-dimensional angiography data set, and the quality of the four-dimensional angiography data set is thus extremely low.
In particular, in interplay with vessel overlap, flow phase overlap distinctly complicates detailed analysis of disease processes, for example, in the analysis of the vessel architecture of an arteriovenous malformation (AVM) with regard to its nidus, associated aneurisms, venous stenoses and the like.
A similar issue may also occur in “2D+t” angiography data sets, e.g., time series of two-dimensional image data subsets which show the progress of contrast agent propagation in the capture region of interest. Rendering time parameters which are descriptive of contrast behavior, in particular, the time profile of contrast agent concentration, in a manner which is intuitive, readily comprehensible and not overshadowed by other effects is in general complex.